STAT 1350 FINAL EXAM NEWEST 2025 ACTUAL EXAM
WITH COMPLETE QUESTIONS AND CORRECT DETAILED
ANSWERS (VERIFIED ANSWERS) ALREADY GRADED A+ |
GUARANTEED PASS
SAMPLING DISTRIBUTION OF THE SAMPLE PROPORTION (Equation) -
Correct Answer > If the sample size (n) is large enough, we know that:
1. The sampling distribution is approximately normal.
2. The mean of the sampling distribution is equal to the population
parameter, p. Expected sample proportion is the same as population
proportion
3. For proportions, the z=(p-hat minus p)/std dev
4. The standard deviation of the sampling distribution is equal to
SAMPLING DISTRIBUTION OF THE SAMPLE MEAN (Equation) - Correct Answer >
If the sample size is large enough, we know that:
1. The sampling distribution is approximately normal.
2. The mean of the sampling distribution is equal to the population
parameter, m (pop mean). **the mean of the mean (x-bar) is the mean
()
Z=(x-bar- /(σ/sqrt(n)) *on scan
, 3. The standard deviation of the sampling distribution is equal to
A LARGE ENOUGH SAMPLE SIZE? - Correct Answer > 1. What exactly does it
mean that our sample size is large enough?
2. This histogram below shows a random sample of 1000 individual
observations from a skewed population.
3. POPULATION IS NOT
NORMAL!!!!!
THE CENTRAL LIMIT THEOREM - Correct Answer > 1. What you just saw was an
illustration of the Central Limit Theorem.
2. You don't need to know the theorem for this class, but it tells us that
with a large enough random sample (As long as n is big enough), the
sampling distribution of a sample mean or a sample proportion will be
approximately normal, even if the original population is not normal.
THE NORMAL APPROXIMATION - Correct Answer > This CHART tells how to find
the mean and standard deviation when we are using the normal
approximation (or when we are assuming a normal sampling
distribution).
WITH COMPLETE QUESTIONS AND CORRECT DETAILED
ANSWERS (VERIFIED ANSWERS) ALREADY GRADED A+ |
GUARANTEED PASS
SAMPLING DISTRIBUTION OF THE SAMPLE PROPORTION (Equation) -
Correct Answer > If the sample size (n) is large enough, we know that:
1. The sampling distribution is approximately normal.
2. The mean of the sampling distribution is equal to the population
parameter, p. Expected sample proportion is the same as population
proportion
3. For proportions, the z=(p-hat minus p)/std dev
4. The standard deviation of the sampling distribution is equal to
SAMPLING DISTRIBUTION OF THE SAMPLE MEAN (Equation) - Correct Answer >
If the sample size is large enough, we know that:
1. The sampling distribution is approximately normal.
2. The mean of the sampling distribution is equal to the population
parameter, m (pop mean). **the mean of the mean (x-bar) is the mean
()
Z=(x-bar- /(σ/sqrt(n)) *on scan
, 3. The standard deviation of the sampling distribution is equal to
A LARGE ENOUGH SAMPLE SIZE? - Correct Answer > 1. What exactly does it
mean that our sample size is large enough?
2. This histogram below shows a random sample of 1000 individual
observations from a skewed population.
3. POPULATION IS NOT
NORMAL!!!!!
THE CENTRAL LIMIT THEOREM - Correct Answer > 1. What you just saw was an
illustration of the Central Limit Theorem.
2. You don't need to know the theorem for this class, but it tells us that
with a large enough random sample (As long as n is big enough), the
sampling distribution of a sample mean or a sample proportion will be
approximately normal, even if the original population is not normal.
THE NORMAL APPROXIMATION - Correct Answer > This CHART tells how to find
the mean and standard deviation when we are using the normal
approximation (or when we are assuming a normal sampling
distribution).
